For the past two decades, the burden of sea-level rise (SLR) on communities has primarily been measured by risk of inundation 1–3. However, mass migration, significant social disruptions, and exacerbated inequities will not be caused by flooded homes alone 4. Moreover, the interconnectedness of coastal communities with their environment will create ripple effects once non-residential assets are inundated, potentially making critical infrastructure less reliable and limiting reliable access to opportunities. This means that inundation-based estimates, for example, 2.8% of the global population 2 and 4.6 million people in the USA, at-risk of inundation by 2100 (with 0.9m of SLR; Hauer et al., 2016), could be too low for understanding SLR burden. To address this, researchers have begun to examine the impacts of SLR on transportation assets, which when inundated, will cause the burden to be more widespread, and to begin sooner than household inundation metrics might suggest (Jasour et al., 2022).
Metrics that focus on SLR inundation of transportation assets include increased commute time and miles of roadway inundation 5,6. As an example, Hauer et al. (2021) found that some commuters in coastal counties may experience an additional 643 minutes of commute time per year by 2060 given current sea-level rise trajectories, due to reduced speeds on inundated roads. Yet, climate adaptation policy is still reliant on estimating the number of displaced people and the timing of this displacement 7. This may be due, in part, to the relative nascent consideration of transportation burden used in the adaptation planning literature. For instance, the research is limited in geographic scope 6,8, does not consider alternative routing due to flooding 5, uses aggregated spatial scales (e.g., census tracts or counties) that make it challenging to decipher distributional impacts on individuals 9,10, or considers only changes in travel time without consideration of congestion and connectivity 9,10.
Isolation provides a complementary measure for the impact of SLR that can offer a more precise spatial and temporal indication of localized burden, along with when the burden may commence. In this work, we appraise coastal “isolation” - meaning a localized lack of physical connectivity by roadway to public accommodations - by building upon the recent literature at the intersection of transportation accessibility and community resilience 11–13. Although isolation has been overlooked in the adaptation literature, isolation is a particularly salient indicator for two reasons. First, isolation due to inundated roadways signals a loss of access to essential services. That is, residents will be unable to reach supermarkets, work, education, healthcare facilities, or be reached by emergency services. Secondly, beyond these losses, it is not guaranteed that residents who experience isolation will be able to safely remain in their homes, even if their home remains dry. Horizontal (i.e., networked) infrastructure is often co-located with roadways; therefore, if a home losses road access, it is possible that other infrastructure services provided to the home are affected (electricity, internet, etc.), even if their homes remain untouched by inundation 14.
Therefore, to better estimate displacement and improve our ability to adapt communities to the impacts of SLR, we ask:
1) How many people are at risk of isolation and at risk of inundation?
2) What is the spatial distribution of people at risk of isolation and how does this compare to people at risk of inundation?
3) What are the temporal differences between the onset of isolation and the onset of inundation?
To address these questions, we use all U.S. coastal counties as a case study and measure the inundation and isolation risk for communities at the census block level (the smallest census geographic unit). More specifically, to measure the risk of isolation, we intersect the OpenStreetMap (OSM) road network for the U.S. with each of NOAA’s mean higher high water (MHHW) scenarios for global mean sea-level (GMSL) rise between 0.3 and 3 meters (1 and 10 feet) and determine whether there is an unflooded path between each block centroid (projected to the closest road) and its closest essential facility (i.e., fire stations, medical services, and primary schools). Not only are these destinations important, but they are also often collocated with community assets that provide wider opportunities, including employment, entertainment, and other essential services. The risk of inundation is measured by intersecting building footprints with NOAA’s MHHW scenarios. The proportion of the block’s 2020 population at risk of inundation is estimated using the proportion of flooded building footprints. This is the finest resolution analysis and contrasts with alternative approaches that use flooded area proportions of census block groups (e.g.,1), thus offering better spatial accuracy.